The Effectiveness of Automated Sonic Bloom Method in An IoT-Based Hydroponic System

One of the solutions for food security is planting using hydroponic method and to increase productivity and help hydroponic grow faster and facilitate in monitoring hydroponic growth, sonic bloom and Internet of Things (IoT) are two technologies that can be used. However, in previous studies, the two systems have not been interconnected. The aim of this study is to evaluate the effectiveness of the combination of the two systems mentioned, hence creating an automated sonic bloom method in an IoT-based hydroponic system. To test the proposed method, this system is implemented with bok choi as the hydroponic plant using the DFT technique. The automated sonic bloom is embedded to the IoT system with DF Player Mini module, RTC module, and speakers. The evaluation is done by comparing growth parameters and the crop parameters. The results show that the system with sonic bloom produces fresh weight of 0,44-0,56 g and dry weight of 0,21–0,33 g. The mentioned results are superior to the system without sonic bloom, where fresh weight is 0,17–0,25 g and dry weight is 0,08–0,13 g. It can be concluded that the IoT-based sonic bloom system is effective in increasing the growth rate and hydroponic production rate.

Multi-variable experimental data set of agronomic data and gaseous soil emissions from maize, oilseed rape and other energy crops at eight sites in Germany

Greenhouse gas emissions (GHG), as well as other gaseous emissions and agronomic variables were continuously measured for three years (2011/2012 – 2014/2015) at eight experimental field sites in Germany. All management activities were consistently documented. The GHG-DB-Thuenen stores these multi-variable data sets of gas fluxes (CO2, N2O, CH4 and NH3), crop parameters (ontogenesis, aboveground biomass, grain and straw yield, N and C content, etc.), soil characteristics (nitrogen content, NH4-N, NO3-N, bulk density etc.), continuously recorded meteorological variables (air and soil temperatures, radiation, precipitation, etc.), management activities (sowing, harvest, soil tillage, fertilization, etc.), and its metadata (methods, further information about variables, etc.). In addition, NOx data were measured and analyzed. Also available are site-specific calculated C and N balances for the respective crops and crop rotations.

Broadacre Mapping of Wheat Biomass Using Ground-Based LiDAR Technology

Crop biomass is an important attribute to consider in relation to site-specific nitrogen (N) management as critical N levels in plants vary depending on crop biomass. Whilst LiDAR technology has been used extensively in small plot-based phenomics studies, large-scale crop scanning has not yet been reported for cereal crops. A LiDAR sensing system was implemented to map a commercial 64-ha wheat paddock to assess the spatial variability of crop biomass. A proximal active reflectance sensor providing spectral indices and estimates of crop height was used as a comparison for the LiDAR system. Plant samples were collected at targeted locations across the field for the assessment of relationships between sensed and measured crop parameters. The correlation between crop biomass and LiDAR-derived crop height was 0.79, which is similar to results reported for plot scanning studies and greatly superior to results obtained for the spectral sensor tested. The LiDAR mapping showed significant crop biomass variability across the field, with estimated values ranging between 460 and 1900 kg ha−1. The results are encouraging for the use of LiDAR technology for large-scale operations to support site-specific management. To promote such an approach, we encourage the development of an automated, on-the-go data processing capability and dedicated commercial LiDAR systems for field operation.

Field Capacity and Efficiency of a Turmeric Rhizome Planter in Response to Machine Speeds and Some Selected Crop Parameters

This study present variation in field capacity and efficiency of a turmeric rhizome planter in response to the machine speed and some selected turmeric parameters (dimension); turmeric rhizome length and diameter. This was done with a view to ascertaining the best condition under which the planter could perform optimally. The land area covered in a specific duration by the planter depends largely on the field capacity. The turmeric rhizome planter consists of trapezoidal hopper, grooved cylindrical metering devise, ground wheels made of mild steel, chain/sprocket drive system, three linkage point and frame. The experiment was randomized in a factorial design of three levels of rhizome lengths of 30, 45 and 60 mm, diameter of 25, 30 and 35 mm and operational speeds of 8, 10, and 12 kmh-1. The result of the study shows that increase in planter operational speed resulted in an increase in field capacity and efficiency of the planter, and had a significant effect on them. The turmeric rhizome lengths and diameter were found to have insignificant effects on the field capacity and efficiency of the planter.

Field Capacity and Efficiency of a Turmeric Rhizome Planter in Response to Machine Speeds and Some Selected Crop Parameters

This study present variation in field capacity and efficiency of a turmeric rhizome planter in response to the machine speed and some selected turmeric parameters (dimension); turmeric rhizome length and diameter. This was done with a view to ascertaining the best condition under which the planter could perform optimally. The land area covered in a specific duration by the planter depends largely on the field capacity. The turmeric rhizome planter consists of trapezoidal hopper, grooved cylindrical metering devise, ground wheels made of mild steel, chain/sprocket drive system, three linkage point and frame. The experiment was randomized in a factorial design of three levels of rhizome lengths of 30, 45 and 60 mm, diameter of 25, 30 and 35 mm and operational speeds of 8, 10, and 12 kmh-1. The result of the study shows that increase in planter operational speed resulted in an increase in field capacity and efficiency of the planter, and had a significant effect on them. The turmeric rhizome lengths and diameter were found to have insignificant effects on the field capacity and efficiency of the planter.

Yield and Quality of Ratoon Sugarcane Are Improved by Applying Potassium under Irrigation to Potassium Deficient Soils

The current study was carried out at the experimental farm of Rana Sugars Ltd., Buttar Seviyan, Amritsar, Punjab, India, to identify methods to improve the yield and quality of ratoon sugarcane in potassium-deficient soils. The treatments comprised two levels of irrigation, resulting in plants which either received sufficient water (I1) or were water-stressed (I2), and four rates of potassium (K) application: 0 (K1), 40 (K2), 80 (K3) and 120 (K4) kg K2O ha−1. The results showed that the irrigation levels did not influence crop parameters significantly, although all parameters presented higher values for I1-treated plots. Compared to the K1 (i.e., 0 kg ha−1 K fertiliser applied) treatment, the K2, K3 and K4 treatments yielded 11.16, 37.9 and 40.7%, respectively, higher millable canes and 1.25, 5.62 and 13.13% more nodes per plant, respectively. At 280 days after harvest of the first (plant) crop, the I1 treatment provided ratoons which were up to 15.58% higher than those obtained with the I2 treatment, with cane girths up to 7.69% wider and yields up to 7.29% higher than those observed with the I2 treatment. While the number of nodes per plant did not differ significantly between treatments, there were significant differences in other parameters. Quality parameters (with the exception of extraction percentage) were significantly enhanced by the K3 treatment. The benefit-to-cost ratio (B/C) was higher for the I1 treatment than for the I2, due to a reduced productivity associated with the I2 treatment. At both irrigation levels, the K3 treatment resulted in the highest quality parameters. K1-, K2- and K4-treated plots presented more instances of insect infestations than plots receiving the K3 treatment. Relative to the K3 plots, infestation by the early shoot borer (Chilo infuscatellus) was 18.2, 6.0 and 12.2% higher, respectively, in plots that underwent the K1, K2 and K4 treatments, while infestation by the top borer (Scirpophaga excerptalis) was 21.2, 9.21 and 14.0% higher, and that by the stalk borer (Chilo auricilius) was 10.7, 0 and 8.10% higher. Not all infestation differences between treatments were significant. Our research demonstrates that growing sugarcane in potassium-deficient soils with applications of 80 kg K2O ha−1 under irrigation should be recommended to increase yield and quality while minimising insect infestation and to implement sustainable ratoon sugarcane production.

Deep Learning-Based Estimation of Crop Biophysical Parameters Using Multi-Source and Multi-Temporal Remote Sensing Observations

Remote sensing data are considered as one of the primary data sources for precise agriculture. Several studies have demonstrated the excellent capability of radar and optical imagery for crop mapping and biophysical parameter estimation. This paper aims at modeling the crop biophysical parameters, e.g., Leaf Area Index (LAI) and biomass, using a combination of radar and optical Earth observations. We extracted several radar features from polarimetric Synthetic Aperture Radar (SAR) data and Vegetation Indices (VIs) from optical images to model crops’ LAI and dry biomass. Then, the mutual correlations between these features and Random Forest feature importance were calculated. We considered two scenarios to estimate crop parameters. First, Machine Learning (ML) algorithms, e.g., Support Vector Regression (SVR), Random Forest (RF), Gradient Boosting (GB), and Extreme Gradient Boosting (XGB), were utilized to estimate two crop biophysical parameters. To this end, crops’ dry biomass and LAI were estimated using three input data; (1) SAR polarimetric features; (2) spectral VIs; (3) integrating both SAR and optical features. Second, a deep artificial neural network was created. These input data were fed to the mentioned algorithms and evaluated using the in-situ measurements. These observations of three cash crops, including soybean, corn, and canola, have been collected over Manitoba, Canada, during the Soil Moisture Active Validation Experimental 2012 (SMAPVEX-12) campaign. The results showed that GB and XGB have great potential in parameter estimation and remarkably improved accuracy. Our results also demonstrated a significant improvement in the dry biomass and LAI estimation compared to the previous studies. For LAI, the validation Root Mean Square Error (RMSE) was reported as 0.557 m2/m2 for canola using GB, and 0.298 m2/m2 for corn using GB, 0.233 m2/m2 for soybean using XGB. RMSE was reported for dry biomass as 26.29 g/m2 for canola utilizing SVR, 57.97 g/m2 for corn using RF, and 5.00 g/m2 for soybean using GB. The results revealed that the deep artificial neural network had a better potential to estimate crop parameters than the ML algorithms.

Rain water use efficiency of sweet potato under cattle-cud mulches in Uyo, Nigeria

Rain Water Use Efficiency (RWUE) of sweet potato under cattle-cud mulches was investigated with the aim of identifying appropriate cattle-cud mulch rates for managing the fragile soils of the humid tropics to ensure sustained productivity of sweet potato. Experimental units measuring 9m2 each were used and sweet potato vines were planted at the spacing of 1 x 0.5m2. Cattle-cud mulches at rates of M0, M10, M20 and M30 tons ha-1 were administered to the different plots. Yield parameters of sweet potatoes as well as rainfall data for the period were collected. Data were assessed using the Analysis of Variance and correlation analyses were also performed to determine the relationships between soil and crop parameters. Results showed that RWUE of 2.38 from M30 plot was significantly higher than other treatments. This correlated with significantly (P<0.05) high tuber girth (19.0cm), tuber length (21.0cm), marketable size (32.33 cm) and number of tuber yield (60.50). Also, organic matter content of the soil receiving M10 (3.60%) and M20 (3.04%) cattle-cud mulches were significantly higher than M0 (2.96%). The use of cattle-cud as a mulch material significantly increased RWUE and also improved the yield and yield component of sweet potato.